In practice, LlamaSim allows you to define multiple AI-powered agents using the Llama model, set up interaction scenarios, and run controlled simulations. You can customize agent personalities, decision-making logic, and communication channels using simple Python APIs. The framework automatically handles prompt construction, response parsing, and conversation state tracking. It logs all interactions and provides built-in evaluation metrics such as response coherence, task completion rate, and latency. With its plugin architecture, you can integrate external data sources, add custom evaluation functions, or extend agent capabilities. LlamaSim’s lightweight core makes it suitable for local development, CI pipelines, or cloud deployments, enabling replicable research and prototype validation.
